Deep residual networks (ResNets) are a special class of CNNs and are deeper, faster to train and easier to optimize than previous CNN architectures [1]}. ResNets employ techniques such as residual learning and identity mapping for shortcut connections [2]}, which enables them to overcome the limitations of traditional CNNs and outperform them in training speed and accuracy. ResFeats, features extracted from the output of convolutional layers of a 50-layer ResNet (ResNet-50), were reported to improve the performance of different image classification tasks in [3]}, including coral classification on the MLC dataset. Although these features are computationally expensive large arrays, we chose to use the image representations extracted from the layers closer to the output end of ResNet-50 to reduce computation cost and alleviate the need for dimensionality reduction.
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